Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment

Vander L S Freitas; Gladston J P Moreira; Leonardo B L Santos

doi:10.7717/peerj.10287

Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment

PeerJ. 2020 Nov 5:8:e10287. doi: 10.7717/peerj.10287. eCollection 2020.

Authors

Vander L S Freitas¹, Gladston J P Moreira¹, Leonardo B L Santos^{2

3}

Affiliations

¹ Department of Computing, Universidade Federal de Ouro Preto, Ouro Preto, Minas Gerais, Brazil.
² National Center for Monitoring and Early Warning of Natural Disasters, Sao Jose dos Campos, Sao Paulo, Brazil.
³ Department of Physics, Humboldt Universität Berlin, Berlin, Germany.

Abstract

We present a robustness analysis of an inter-cities mobility complex network, motivated by the challenge of the COVID-19 pandemic and the seek for proper containment strategies. Brazilian data from 2016 are used to build a network with more than five thousand cities (nodes) and twenty-seven states with the edges representing the weekly flow of people between cities via terrestrial transports. Nodes are systematically isolated (removed from the network) either at random (failures) or guided by specific strategies (targeted attacks), and the impacts are assessed with three metrics: the number of components, the size of the giant component, and the total remaining flow of people. We propose strategies to identify which regions should be isolated first and their impact on people mobility. The results are compared with the so-called reactive strategy, which consists of isolating regions ordered by the date the first case of COVID-19 appeared. We assume that the nodes' failures abstract individual municipal and state initiatives that are independent and possess a certain level of unpredictability. Differently, the targeted attacks are related to centralized strategies led by the federal government in agreement with municipalities and states. Removing a node means completely restricting the mobility of people between the referred city/state and the rest of the network. Results reveal that random failures do not cause a high impact on mobility restraint, but the coordinated isolation of specific cities with targeted attacks is crucial to detach entire network areas and thus prevent spreading. Moreover, the targeted attacks perform better than the reactive strategy for the three analyzed robustness metrics.

Keywords: COVID-19; Complex networks; Geographical networks; Mobility networks; Robustness analysis; SARS-CoV-2.

Grants and funding

This work was supported by the Sao Paulo Research Foundation (FAPESP), Grant Numbers 2015/50122-0 and 2018/06205-7; DFG-IRTG Grant Number 1740/2; CNPq Grant Number 420338/2018-7; CAPES Grant Number 23038.014333/2020-46. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.